Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph-a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. Read More

Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom.

Electroencephalography (EEG) recordings represent a vital component of the assessment of sleep physiology, but the methodology presently used is costly, intrusive to participants, and laborious in application. There is a recognized need to develop more easily applicable yet reliable EEG systems that allow unobtrusive long-term recording of sleep-wake EEG ideally away from the laboratory setting. cEEGrid is a recently developed flex-printed around-the-ear electrode array, which holds great potential for sleep-wake monitoring research. Read More

Objective:: In elderly patients, women have better qualities of sleep than men in objective parameters; however, women subjectively complain more about sleep disturbances than men. We performed visual scoring and spectral analysis of sleep electroencephalograms to explain these gender differences in the degree of arousal, the most representative marker in insomnia.

Methods:: A total of 354 participants (≥60 years old) were recruited from a Korean community underwent nocturnal polysomnography (NPSG). Read More

Authors:

School of Control Science and Engineering, Shandong University, Jinan, People's Republic of China. School of Information Technology and Electrical Engineering, University of Queensland, Queensland, Australia.

Objective: Sleep quality helps to reflect on the physical and mental condition, and efficient sleep stage scoring promises considerable advantages to health care. The aim of this study is to propose a simple and efficient sleep classification method based on entropy features and a support vector machine classifier, named SC-En&SVM.

Approach: Entropy features, including fuzzy measure entropy (FuzzMEn), fuzzy entropy, and sample entropy are applied for the analysis and classification of sleep stages. Read More

Authors:

Chronobiology and Sleep Research, Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.

The classification of sleep stages is the first and an important step in the quantitative analysis of polysomnographic recordings. Sleep stage scoring relies heavily on visual pattern recognition by a human expert and is time consuming and subjective. Thus, there is a need for automatic classification. Read More

Authors:

Overnight polysomnography (PSG) is the gold standard tool used to characterize sleep and for diagnosing sleep disorders. PSG is a non-invasive procedure that collects various physiological data which is then scored by sleep specialists who assign a sleep stage to every 30-second window of the data according to predefined scoring rules. In this study, we aimed to automate the process of sleep stage scoring of overnight PSG data while adhering to expert-based rules. Read More

Authors:

Sleep spindles result from interactions between the thalamic and cortical neurons during the NREM2 stage. Studies show that these waxing and waning episodes of field potentials may have an implied role in memory consolidation, cellular plasticity and neuronal development besides serving as important markers in several neuronal pathologies. For these reasons, accurate spindle scoring of polysomnographic signals is important and has garnered interest in automating the tedious process of scoring via visual inspection. Read More

Authors:

Chronic insomnia can significantly impair an individual's quality of life leading to a high societal cost. Unfortunately, limited automated tools exist that can assist clinicians in the timely detection of insomnia. In this paper, we propose a two stage approach to automatically detect insomnia from an overnight EEG recording. Read More

Authors:

Traditional manual scoring of the entire sleep for diagnosis of sleep disorders is highly time-consuming and dependent to experts experience. Thus, automatic methods based on electrooculography (EOG) analysis have been increasingly attracted attentions to lower the cost of scoring. Such computeraided diagnosis of sleep disorders are usually based on the 6 scores, wake (W), sleep status (S1-S4) and REM by labelling every 30-second long EOG records. Read More

Disrupted sleep is a contributing factor to cognitive ageing, while also being associated with neurodegenerative disorders. Little is known, however, about the relation of sleep and the gradual cognitive changes over the adult life course. Sleep electroencephalogram (EEG) patterns are potential markers of the cognitive progress. Read More

Authors:

Department of Respiratory Medicine, NHS Dumfries and Galloway, United Kingdom.

Study Objectives: Guidelines recommend regular review of individuals using continuous positive airway pressure (CPAP) to treat obstructive sleep apnea but do not agree on the core components and frequency. We aimed to achieve consensus on essential components and frequency of review.

Methods: We used an e-Delphi approach, recruiting a multidisciplinary international expert panel to identify components based on a list compiled from guidelines and to score these on a scale 1 to 5 over three rounds. Read More

The reference standard for sleep classification uses manual scoring of polysomnography with fixed 30-s epochs. This limits the analysis of sleep pattern, structure and, consequently, detailed association with other physiologic processes. We aimed to improve the details of sleep evaluation by developing a data-driven method that objectively classifies sleep in smaller time intervals. Read More

Authors:

School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China; Research Institute of Beihang University in Shenzhen and the Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and the State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 100191, China; Microsoft Research Asia, Beijing, 100080, China. Electronic address:

Background: Automatic sleep stage classification is essential for long-term sleep monitoring. Wearable devices show more advantages than polysomnography for home use. In this paper, we propose a novel method for sleep staging using heart rate and wrist actigraphy derived from a wearable device. Read More

Background: Previous functional magnetic resonance imaging (fMRI) sleep studies have been hampered by the difficulty of obtaining extended amounts of sleep in the sleep-adverse environment of the scanner and often have resorted to manipulations such as sleep depriving subjects before scanning. These manipulations limit the generalizability of the results.

New Method: The current study is a methodological validation of procedures aimed at obtaining all-night fMRI data in sleeping subjects with minimal exposure to experimentally induced sleep deprivation. Read More

Sleep stage classification is an important task for the timely diagnosis of sleep disorders and sleep-related studies. In this paper, automatic scoring of sleep stages using Electrooculogram (EOG) is presented. Single channel EOG signals are analyzed in Discrete Wavelet Transform (DWT) domain employing various statistical features such as Spectral Entropy, Moment-based Measures, Refined Composite Multiscale Dispersion Entropy (RCMDE) and Autoregressive (AR) Model Coefficients. Read More

Background: The first edition of the European position paper (EPP) on drug-induced sleep endoscopy (DISE) was published in 2014 with the aim to standardise the procedure, to provide an in-depth insight into the main aspects of this technique and to have a basis for future research. Since 2014, new studies have been published concerning new sedative agents or new insights into the pattern/levels of the obstruction depending on the depth of sedation. Therefore, an enlarged group of European experts in the field of sleep breathing disorders (SBD), including the most of the first DISE EPP main authors, has decided to publish an update of the European position paper on DISE, in order to include new evidence and to find a common language useful for reporting the findings of this endoscopic evaluation in adult population affected by SBD. Read More

Objective: Neonates spend most of their time asleep. Sleep of preterm infants evolves rapidly throughout maturation and plays an important role in brain development. Since visual labelling of the sleep stages is a time consuming task, automated analysis of electroencephalography (EEG) to identify sleep stages is of great interest to clinicians. Read More

Objectives: Polysomnography is essential to diagnose sleep disorders. It is used to identify a patient's sleep pattern during sleep. This pattern is obtained by a doctor or health practitioner by using a scoring process, which is time consuming. Read More

Background: Sleep spindles are a marker of stage 2 NREM sleep that are linked to learning & memory and are altered by many neurological diseases. Although visual inspection of the EEG is considered the gold standard for spindle detection, it is time-consuming, costly and can introduce inter/ra-scorer bias.

New Method: Our goal was to develop a simple and efficient sleep-spindle detector (algorithm #7, or 'A7') that emulates human scoring. Read More

Authors:

Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran.

Introduction: Sleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques.

Methods: Sleep-EDF polysomnography was used in this study as a dataset. Read More

The domestic dog () has been shown to both excel in recognising human emotions and produce emotion-related vocalisations and postures that humans can easily recognise. However, little is known about the effect of emotional experiences on subsequent sleep physiology, a set of phenomena heavily interrelated with emotions in the case of humans. The present paper examines heart rate (HR) and heart rate variability (HRV) during dogs' sleep, measures that are influenced by both positive and negative emotions in awake dogs. Read More

Objective: A higher density of sleep electroencephalogram (EEG) spindles has been cross-sectionally associated with more efficient cortical-subcortical connectivity, superior intellectual and learning abilities, and healthier emotional and behavioral traits. In the present study, we explored to what extent sleep spindle density (SSD) at age five years could predict emotional and behavioral traits at six and nine years.

Methods: A total of 19 healthy preschoolers at age five years underwent in-home sleep EEG recordings for visual scoring of non-rapid eye movement stage 2 (NREM-S2) sleep spindles, and SSD in NREM-S2 was calculated. Read More

Authors:

Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States.

Veterans with posttraumatic stress disorder (PTSD) often report suboptimal sleep quality, often described as lack of restfulness for unknown reasons. These experiences are sometimes difficult to objectively quantify in sleep lab assessments. Here, we used a streamlined sleep assessment tool to record in-home 2-channel electroencephalogram (EEG) with concurrent collection of electrodermal activity (EDA) and acceleration. Read More

Authors:

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia. Electronic address:

Sleep related disorder causes diminished quality of lives in human beings. Sleep scoring or sleep staging is the process of classifying various sleep stages which helps to detect the quality of sleep. The identification of sleep-stages using electroencephalogram (EEG) signals is an arduous task. Read More

Background: The standard method for scoring polysomnographic (PSG) sleep is insufficient in the intensive care unit (ICU). A modified classification has been proposed, but has not been tested in specific groups of ICU patients. We aimed firstly to (1) use the modified classification to describe sleep in two groups of ICU patients: a severe sepsis group and a chronic obstructive pulmonary disease (COPD) group, and (2) to compare sleep stage distribution in the groups; secondly to compare the PSG findings with nurses' sleep evaluation. Read More

Authors:

Department of Otorhinolaryngology, Head and Neck Surgery, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany.

Classic visual sleep stage scoring is based on electroencephalogram (EEG) frequency band analysis of 30 s epochs and is commonly performed by highly trained medical sleep specialists using additional information from submental EMG and eye movements electrooculogram (EOG). In this study, we provide the proof-of-principle in 40 subjects that sleep stages can be consistently differentiated solely on the basis of spatial 3-channel EEG patterns based on root-mean-square (RMS) amplitudes. The polysomnographic 3-channel EEG data are pre-processed by RMS averaging over intervals of 30 s leading to spatial cortical activity patterns represented by 3-dimensional vectors. Read More

Objective: To compare conditional random fields (CRF), hidden Markov models (HMMs) and Bayesian linear discriminants (LDs) for cardiorespiratory sleep stage classification on a five-class sleep staging task (wake/N1/N2/N3/REM), to explore the benefits of incorporating time information in the classification and to evaluate the feasibility of sleep staging on obstructive sleep apnea (OSA) patients.

Approach: The classifiers with and without time information were evaluated with 10-fold cross-validation on five-, four- (wake/N1 + N2/N3/REM) and three-class (wake/NREM/REM) classification tasks using a data set comprising 443 night-time polysomnography (PSG) recordings of 231 participants (180 healthy participants, 100 of which had a 'regular' sleep architecture, and 51 participants previously diagnosed with OSA).

Main Results: CRF with time information (CRFt) outperforms all other classifiers on all tasks, achieving a median accuracy and Cohen's κ for all participants of 62. Read More

Authors:

Objective: the recent emergence and success of electroencephalography (EEG) in low-cost portable devices, has opened the door to a new generation of applications processing a small number of EEG channels for health monitoring and brain-computer interfacing. These recordings are, however, contaminated by many sources of noise degrading the signals of interest, thus compromising the interpretation of the underlying brain state. In this paper, we propose a new data-driven algorithm to effectively remove ocular and muscular artifacts from single-channel EEG: the surrogate-based artifact removal (SuBAR). Read More

Authors:

Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; School of Psychology, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom.

Objective: Limitations of the manual scoring of polysomnograms, which include data from electroencephalogram (EEG), electro-oculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG) channels have long been recognized. Manual staging is resource intensive and time consuming, and thus considerable effort must be spent to ensure inter-rater reliability. As a result, there is a great interest in techniques based on signal processing and machine learning for a completely Automatic Sleep Stage Classification (ASSC). Read More

Objectives: Spinocerebellar ataxias are progressive neurodegenerative disorders characterized by progressive cerebellar features with additional neuro-axis involvement. Oculomotor abnormality is one of the most frequent manifestations. This study was done to assess the polysomnographic abnormalities in patients with Spinocerebellar ataxia (SCA1, SCA2 and SCA3) and also to evaluate whether oculomotor abnormalities interfere with sleep stage R scoring. Read More

Authors:

During the past decades, a great body of research has been devoted to automatic sleep stage scoring using the electroencephalogram (EEG). However, the results are not yet satisfactory to be used as a standard procedure in clinical studies. In this study, using recent developments in robust EEG phase extraction, a novel set of EEG-based features containing the Shannon entropy of the instantaneous analytical form envelope and frequencies of the EEG are proposed for sleep stage scoring. Read More

Authors:

Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Study Objectives: The American Academy of Sleep Medicine has published manuals for scoring polysomnograms that recommend time spent in non-rapid eye movement sleep stages (stage N1, N2, and N3 sleep) be reported. Given the well-established large interrater variability in scoring stage N1 and N3 sleep, we determined the range of time in stage N1 and N3 sleep scored by a large number of technologists when compared to reasonably estimated true values.

Methods: Polysomnograms of 70 females were scored by 10 highly trained sleep technologists, two each from five different academic sleep laboratories. Read More

Authors:

Objective: We developed and implemented two predictor-corrector methods for the classification of two-channel EEG data into sleep stages.

Approach: The sequence of sleep stages over the night is modeled by a Markov chain of first and second order, resulting in an informative prior distribution for the new state, given the distribution of the current one. The correction step is realized by applying a Bayes classifier using the (preprocessed) data and this prior. Read More

Authors:

Quantifying mental alertness in today's world is important as it enables the person to adopt lifestyle changes for better work efficiency. Miniaturized sensors in wearable devices have facilitated detection/monitoring of mental alertness. Photoplethysmography (PPG) sensors through Heart Rate Variability (HRV) offer one such opportunity by providing information about one's daily alertness levels without requiring any manual interference from the user. Read More

Authors:

Department of Obstetrics and Gynecology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

Purpose: The symptom clusters in patients with ovarian cancer undergoing chemotherapy have not been well evaluated. We investigated the symptom clusters and effects of symptom clusters on the quality of life of patients with ovarian cancer.

Method: We recruited 210 ovarian cancer patients being treated with chemotherapy and used a descriptive cross-sectional study design to collect information on their symptoms. Read More

Authors:

Study Objectives: Automated sleep staging has been previously limited by a combination of clinical and physiological heterogeneity. Both factors are in principle addressable with large data sets that enable robust calibration. However, the impact of sample size remains uncertain. Read More

Authors:

Department of Electrical and Electronic EngineeringImperial College London.

The monitoring of sleep patterns without patient's inconvenience or involvement of a medical specialist is a clinical question of significant importance. To this end, we propose an automatic sleep stage monitoring system based on an affordable, unobtrusive, discreet, and long-term wearable in-ear sensor for recording the electroencephalogram (ear-EEG). The selected features for sleep pattern classification from a single ear-EEG channel include the spectral edge frequency and multi-scale fuzzy entropy, a structural complexity feature. Read More

Background: Sleep and sleep quality assessment by means of sleep stage analysis is important for both scientific and clinical applications. Unfortunately, the presently preferred method, polysomnography (PSG), requires considerable expert assistance and significantly affects the sleep of the person under observation. A reliable, accurate and mobile alternative to the PSG would make sleep information much more readily available in a wide range of medical circumstances. Read More

Authors:

Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.

Although quantitative analysis of the sleep electroencephalogram (EEG) has uncovered important aspects of brain activity during sleep in adolescents and adults, similar findings from preschool-age children remain scarce. This study utilized our time-frequency method to examine sleep oscillations as characteristic features of human sleep EEG. Data were collected from a longitudinal sample of young children ( = 8; 3 males) at ages 2, 3, and 5 years. Read More

The concurrent usage of actigraphy and heart rate variability (HRV) for sleep efficiency quantification is still matter of investigation. This study compared chest (CACT) and wrist (WACT) actigraphy (actigraphs positioned on chest and wrist, respectively) in combination with HRV for automatic sleep vs wake classification. Accelerometer and ECG signals were collected during polysomnographic studies (PSGs) including 18 individuals (25-53 years old) with no previous history of sleep disorders. Read More

Study Objectives: To compare the accuracy of automatic sleep staging based on heart rate variability measured from photoplethysmography (PPG) combined with body movements measured with an accelerometer, with polysomnography (PSG) and actigraphy.

Authors:

This paper proposes a practical approach to addressing limitations posed by using of single-channel electroencephalography (EEG) for sleep stage classification. EEG-based characterizations of sleep stage progression contribute the diagnosis and monitoring of the many pathologies of sleep. Several prior reports explored ways of automating the analysis of sleep EEG and of reducing the complexity of the data needed for reliable discrimination of sleep stages at lower cost in the home. Read More

Objective: This preliminary study investigated electrophysiological and microstructural features of sleep in children and adolescents 4-18 years of age who were born to depressed mothers.

Methods: A total of 31 healthy subjects (15 male and 16 female) participated in the study. In this sample, 20 children born to mothers diagnosed with Major Depressive Disorder (MDD) were designated as "high-risk"; 11 children born to mothers without a personal history of depression were designated as "low-risk. Read More

Authors:

This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG. Most of the existing methods rely on hand-engineered features, which require prior knowledge of sleep analysis. Only a few of them encode the temporal information, such as transition rules, which is important for identifying the next sleep stages, into the extracted features. Read More